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You do not need one universal Linux PC specification for a local AI coding assistant. Choose hardware from the model size, context length and workflow first. As a practical starting point, Ollama lists 8 GB of RAM for 7B models, 16 GB for 13B and 32 GB for 33B; CPU-only operation is supported, while a compatible GPU can accelerate inference. Long-context agentic coding can change the requirement dramatically: Ollama reports about 23 GB of VRAM for glm-4.7-flash at a 64,000-token context, an example that applies to that model and context rather than to every model.
Start with the model and workflow
A local coding assistant is a workload, not a fixed appliance. The hardware that feels comfortable for short autocomplete suggestions may be inadequate for an agent that reads a repository, keeps a long conversation and performs several tool calls. Before buying or upgrading anything, decide which of these workflows you actually need:
- Autocomplete or short chat: a smaller model, modest context and occasional questions. System RAM is usually the first constraint.
- Repository chat: the assistant must retain more files and conversation history. Context length raises memory use even when the model itself is unchanged.
- Agentic coding: the model plans, calls tools, edits files and repeats the cycle. Ollama’s January 2026 coding-launch guidance recommends at least 64,000 tokens for the coding-tool configuration it describes. That capacity can require substantially more memory than autocomplete.
Model size, quantization, context length, simultaneous requests and the runtime’s acceleration backend all affect the result. There is no evidence-based universal Linux minimum or reliable speed ranking for every CPU, GPU and model combination, so treat the figures below as planning guidance rather than guarantees.
How much RAM do you need?
RAM is the most useful first filter because a CPU run, operating system, editor, runtime and model must share it. Ollama’s quickstart gives these broad starting points:
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| Model size | Ollama quickstart starting RAM | What the figure means |
|---|---|---|
| 7B | At least 8 GB | A broad model-size starting point, not a guarantee of a responsive coding session. |
| 13B | At least 16 GB | Leaves less headroom for the desktop, IDE and long prompts than the number alone suggests. |
| 33B | At least 32 GB | A starting point for the model class; context length and concurrent work can push usage higher. |
These values are not a complete system specification. Linux services, the editor, browser tabs, language servers and file indexes consume memory before inference begins. If the runtime has to swap model data to disk, interaction can become impractical even though the model technically loads. For a machine you already own, begin with a smaller model and watch actual usage instead of assuming that the quickstart number leaves a fixed amount of spare capacity.
RAM planning by practical tier
- 8 GB total: feasible only for the smallest supported models and light prompts. Keep the desktop and other applications lean, and expect to reduce context if memory pressure appears.
- 16 GB total: a more workable entry point for 7B-class coding models, provided the context is moderate and you are not running several heavy development services.
- 32 GB total: gives 13B-class experiments and larger contexts more room, but does not automatically make a 33B model or a 64,000-token agent practical.
- 64 GB or more: useful when repository indexing, containers, compilers and a larger local model must coexist. It still cannot substitute for sufficient VRAM when you intend to keep a large model on a GPU.
Can you run a local coding assistant without a GPU?
Yes. Ollama documents CPU inference on Linux, so a dedicated graphics card is optional. CPU-only operation is a sensible way to validate a model and workflow on an existing or modest PC. The trade-off is responsiveness: larger models and long contexts require more computation and memory bandwidth, and the available documentation does not establish a universal tokens-per-second result for particular Linux CPUs.
When a supported GPU is present, Ollama documents optional CUDA, ROCm and Vulkan backends. Acceleration is conditional on the card, driver and software stack; do not assume that every card from a given vendor works out of the box. Check the current backend prerequisites for the exact GPU and Linux distribution, then confirm that the runtime actually uses it.
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How much VRAM do local coding models need?
VRAM is model- and context-dependent. It is not possible to turn one example into a general minimum. Ollama’s January 23, 2026 launch article gives a specific planning figure: about 23 GB of VRAM for glm-4.7-flash with a 64,000-token context. The model name and context length are essential qualifications. A different model, quantization or context can require materially more or less memory.
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For that particular long-context target, searching for a graphics card with at least 24 GB of VRAM is a reasonable capacity criterion, with little room left for other GPU workloads. It is not a recommendation that every Linux coding setup needs a 24 GB card. For smaller models or shorter contexts, system RAM and a supported acceleration path may matter more than very large VRAM.
Usable VRAM can also be lower than the advertised capacity after the desktop, display server and other GPU applications claim memory. Leave headroom rather than planning to fill every last gigabyte. If the model does not fit, the runtime may place part of it in system memory, fall back to CPU work or fail to load; the behavior depends on the runtime and configuration.
Why context length changes the hardware requirement
Context is the amount of tokenized conversation and source material the model can consider at once. A longer window lets an agent see more files and history, but the associated working memory grows. Ollama states that required RAM scales with both parallel requests and context length. This is why a model that fits comfortably for a short chat can become impractical when configured for a large repository.
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When memory is insufficient
- Lower the runtime or integration’s context-length setting.
- Choose a smaller model or a model variant with a lower memory footprint.
- Close memory-heavy applications and stop unused containers or virtual machines.
- Reduce parallel requests; each active request can add memory pressure.
- Check whether the model is being partially offloaded instead of fully accelerated.
Continue’s Linux integration guide specifically suggests lowering contextLength or selecting a smaller model when memory is tight. Its examples are based on Continue 1.1.x and Ollama 0.11.x, so treat the exact configuration keys and model tags as version-specific and verify them against the versions you install.
Storage: model files are separate from RAM and VRAM
Downloaded model files occupy persistent disk space even when a model is not loaded. Ollama’s quickstart illustrates the range: its example download for Llama 3.1 8B is 4.7 GB, while the 70B example is 40 GB. Other models can be smaller or reach tens or hundreds of gigabytes. Keep free space for multiple model variants, temporary files, logs and normal Linux updates.
An SSD is preferable for installation, model loading and swapping, but faster storage does not remove the memory requirement. If a model repeatedly spills into swap, adding disk capacity alone will not produce the experience of having enough RAM or VRAM. Before downloading, check the model’s displayed size and keep additional headroom rather than allocating the entire disk to model files.
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| Axis | What to check | Why it matters |
|---|---|---|
| System RAM | Installed memory minus the desktop, IDE, language servers and other services | CPU inference and model data compete with the rest of the system. |
| GPU VRAM | Capacity available after display and other GPU workloads | Determines whether a model and its context can stay on the GPU. |
| Acceleration backend | CUDA, ROCm or Vulkan support, plus matching drivers and runtime versions | A compatible card without a working backend may run as CPU-only. |
| Model download size | Exact tag and file size, with free SSD capacity | Large models can consume tens or hundreds of gigabytes. |
| Context target | Short prompts, repository chat or 64,000-token agentic work | Memory use rises as the context window and parallel requests grow. |
| Workflow | Autocomplete/chat versus tool-using agent | Agents generally need more context and sustained memory headroom. |
Do not use an advertised CPU core count as a substitute for these measurements. The available guidance does not provide comparable Linux benchmarks across CPUs, GPUs, quantizations and coding-agent workloads, so a claimed speed winner would be unsupported.
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Install and check a local runtime on Linux
Ollama’s quickstart documents this installation command:
curl -fsSL https://ollama.com/install.sh | shReview an install script before executing it if your organization’s security policy requires that. After installation, use the runtime’s own commands to download and inspect models:
- Pull the exact model tag you intend to use:
ollama pull <model-tag>. Copy the tag exactly from the model documentation or your editor integration. - List downloaded models:
ollama list. Confirm the expected tag and note the stored size. - Run a controlled prompt: start with a small file and a modest context so you can observe behavior before indexing an entire repository.
- Inspect memory placement:
ollama psshows the running model and its memory use. Use it while testing CPU and GPU configurations. - Increase context gradually: raise the integration’s context setting only while the system remains responsive and has swap headroom.
A model-not-found error commonly means the integration’s tag does not exactly match the tag you pulled. Correct the configuration or pull the requested tag; changing unrelated hardware will not fix a naming mismatch.
Choose hardware by the setup you actually have
Existing or modest Linux PC
Start with a smaller model and CPU inference. Keep context conservative, close unnecessary applications and use ollama ps to observe memory use. This path answers whether local assistance fits your privacy, latency and workflow needs before any upgrade.
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Midrange system with optional GPU
Match model size to available system RAM, then verify that the GPU has a supported CUDA, ROCm or Vulkan path and enough free VRAM for the chosen context. A GPU may improve responsiveness, but no universal VRAM threshold is established for all models. Test the exact tag and context rather than buying to a generic model-size label.
Large model with long context
Treat VRAM as a primary selection constraint. For the documented glm-4.7-flash, 64,000-token example, approximately 23 GB of VRAM is reported; a card marketed with at least 24 GB is therefore a capacity-oriented search criterion for that specific target. Also budget substantial system RAM, storage and cooling for the rest of the development stack.
A pre-upgrade checklist
- Write down the exact model tag, parameter class and quantization.
- Decide whether you need autocomplete, repository chat or an agent with tools.
- Set a context target; do not assume the model’s maximum is necessary.
- Measure installed RAM, free SSD space and available VRAM while your normal development tools are open.
- Verify the current Linux driver and runtime backend for your exact GPU.
- Download one model, test a representative coding task and inspect
ollama ps. - If memory is tight, lower
contextLength, reduce parallel requests or choose a smaller model before replacing hardware.
What the numbers do—and do not—promise
The 8/16/32 GB RAM figures are Ollama quickstart guidance for 7B, 13B and 33B model classes, not guarantees for an interactive coding setup. The 4.7 GB and 40 GB download examples describe specific Llama 3.1 files, not all models. The approximately 23 GB VRAM figure describes glm-4.7-flash at 64,000 tokens, not a general minimum. Finally, the 64,000-token recommendation belongs to Ollama’s described coding-tool workflow. Keeping those qualifications attached prevents an expensive mismatch between a specification sheet and the workload you intend to run.
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